Mixture Reduction Algorithms for Uncertain Tracking
Abstract
Bayesian solutions of tracking problems that involve measurement association uncertainty, give rise to Gaussian mixture distributions, which are composed of an ever increasing number of components. To implement such a tracking filter, the growth of components must be controlled by approximating the mixture distribution. A popular and economical scheme is the Probabilistic Data Association Filter (PDAD), which reduces the mixture to a single Gaussian component at each time step. However, this approximation may destroy valuable information, especially if several significant, well spaced components are present. In this report, two new algorithms for reducing Gaussian mixture distributions are presented. These techniques preserve the mean and covariance of the original mixture, and the final approximation is itself a Gaussian mixture. The reduction is achieved by successively merging components or groups of components. The two algorithms have been used to control the growth of components which occurs with the solution to the problem of tracking a single object, in the presence of uniformly distributed false measurements. Simulation results are presented which compare the performance of the resulting tracking filters and the PDAF.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1988
- Accession Number
- ADA197641
Entities
People
- D. J. Salmond
Organizations
- Royal Aircraft Establishment